Overview

Dataset statistics

Number of variables33
Number of observations1100000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory841.6 MiB
Average record size in memory802.3 B

Variable types

DateTime1
Categorical13
Numeric16
Text3

Alerts

category is highly overall correlated with subcategoryHigh correlation
channel is highly overall correlated with city and 4 other fieldsHigh correlation
city is highly overall correlated with channel and 4 other fieldsHigh correlation
country is highly overall correlated with channel and 4 other fieldsHigh correlation
discount_pct is highly overall correlated with promo_flagHigh correlation
gross_sales is highly overall correlated with list_price and 3 other fieldsHigh correlation
is_weekend is highly overall correlated with weekdayHigh correlation
latitude is highly overall correlated with channel and 4 other fieldsHigh correlation
list_price is highly overall correlated with gross_sales and 2 other fieldsHigh correlation
longitude is highly overall correlated with channel and 4 other fieldsHigh correlation
margin_pct is highly overall correlated with promo_flagHigh correlation
month is highly overall correlated with weekofyearHigh correlation
net_sales is highly overall correlated with gross_sales and 3 other fieldsHigh correlation
promo_flag is highly overall correlated with discount_pct and 1 other fieldsHigh correlation
purchase_cost is highly overall correlated with gross_sales and 2 other fieldsHigh correlation
store_id is highly overall correlated with channel and 4 other fieldsHigh correlation
subcategory is highly overall correlated with categoryHigh correlation
units_sold is highly overall correlated with gross_sales and 1 other fieldsHigh correlation
weekday is highly overall correlated with is_weekendHigh correlation
weekofyear is highly overall correlated with monthHigh correlation
is_holiday is highly imbalanced (89.6%)Imbalance
discount_pct is highly imbalanced (75.7%)Imbalance
promo_flag is highly imbalanced (59.7%)Imbalance
stock_out_flag is highly imbalanced (80.5%)Imbalance
weekday has 156713 (14.2%) zerosZeros

Reproduction

Analysis started2025-12-28 15:29:43.077018
Analysis finished2025-12-28 15:33:04.375866
Duration3 minutes and 21.3 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

date
Date

Distinct1095
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size8.4 MiB
Minimum2021-01-01 00:00:00
Maximum2023-12-31 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-12-28T22:33:04.463630image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-28T22:33:04.609922image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

year
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size55.6 MiB
2021
366825 
2022
366715 
2023
366460 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters4400000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021
2nd row2021
3rd row2021
4th row2021
5th row2021

Common Values

ValueCountFrequency (%)
2021366825
33.3%
2022366715
33.3%
2023366460
33.3%

Length

2025-12-28T22:33:04.735261image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-28T22:33:04.823672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2021366825
33.3%
2022366715
33.3%
2023366460
33.3%

Most occurring characters

ValueCountFrequency (%)
22566715
58.3%
01100000
25.0%
1366825
 
8.3%
3366460
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number4400000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
22566715
58.3%
01100000
25.0%
1366825
 
8.3%
3366460
 
8.3%

Most occurring scripts

ValueCountFrequency (%)
Common4400000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
22566715
58.3%
01100000
25.0%
1366825
 
8.3%
3366460
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII4400000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
22566715
58.3%
01100000
25.0%
1366825
 
8.3%
3366460
 
8.3%

month
Real number (ℝ)

High correlation 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5256127
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2025-12-28T22:33:04.900924image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4477598
Coefficient of variation (CV)0.52834269
Kurtosis-1.2069738
Mean6.5256127
Median Absolute Deviation (MAD)3
Skewness-0.010304752
Sum7178174
Variance11.887048
MonotonicityNot monotonic
2025-12-28T22:33:04.987087image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
193434
8.5%
393434
8.5%
793434
8.5%
593434
8.5%
893434
8.5%
1093403
8.5%
1293403
8.5%
490420
8.2%
690420
8.2%
990402
8.2%
Other values (2)174782
15.9%
ValueCountFrequency (%)
193434
8.5%
284392
7.7%
393434
8.5%
490420
8.2%
593434
8.5%
690420
8.2%
793434
8.5%
893434
8.5%
990402
8.2%
1093403
8.5%
ValueCountFrequency (%)
1293403
8.5%
1190390
8.2%
1093403
8.5%
990402
8.2%
893434
8.5%
793434
8.5%
690420
8.2%
593434
8.5%
490420
8.2%
393434
8.5%

day
Real number (ℝ)

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.720444
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2025-12-28T22:33:05.084721image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.7962619
Coefficient of variation (CV)0.55954286
Kurtosis-1.1931587
Mean15.720444
Median Absolute Deviation (MAD)8
Skewness0.0075380274
Sum17292488
Variance77.374224
MonotonicityNot monotonic
2025-12-28T22:33:05.200904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
136165
 
3.3%
236165
 
3.3%
336165
 
3.3%
436165
 
3.3%
536165
 
3.3%
636165
 
3.3%
736165
 
3.3%
836165
 
3.3%
936165
 
3.3%
1036165
 
3.3%
Other values (21)738350
67.1%
ValueCountFrequency (%)
136165
3.3%
236165
3.3%
336165
3.3%
436165
3.3%
536165
3.3%
636165
3.3%
736165
3.3%
836165
3.3%
936165
3.3%
1036165
3.3%
ValueCountFrequency (%)
3121096
1.9%
3033150
3.0%
2933150
3.0%
2836164
3.3%
2736164
3.3%
2636164
3.3%
2536164
3.3%
2436164
3.3%
2336164
3.3%
2236164
3.3%

weekofyear
Real number (ℝ)

High correlation 

Distinct53
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.570811
Minimum1
Maximum53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2025-12-28T22:33:05.327519image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q114
median27
Q340
95-th percentile50
Maximum53
Range52
Interquartile range (IQR)26

Descriptive statistics

Standard deviation15.051255
Coefficient of variation (CV)0.56645826
Kurtosis-1.2001142
Mean26.570811
Median Absolute Deviation (MAD)13
Skewness0.00063724589
Sum29227892
Variance226.54028
MonotonicityNot monotonic
2025-12-28T22:33:05.461678image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
121098
 
1.9%
321098
 
1.9%
221098
 
1.9%
421098
 
1.9%
521098
 
1.9%
1321098
 
1.9%
621098
 
1.9%
721098
 
1.9%
821098
 
1.9%
921098
 
1.9%
Other values (43)889020
80.8%
ValueCountFrequency (%)
121098
1.9%
221098
1.9%
321098
1.9%
421098
1.9%
521098
1.9%
621098
1.9%
721098
1.9%
821098
1.9%
921098
1.9%
1021098
1.9%
ValueCountFrequency (%)
533015
 
0.3%
5221091
1.9%
5121091
1.9%
5021091
1.9%
4921091
1.9%
4821091
1.9%
4721091
1.9%
4621091
1.9%
4521091
1.9%
4421091
1.9%

weekday
Real number (ℝ)

High correlation  Zeros 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0054791
Minimum0
Maximum6
Zeros156713
Zeros (%)14.2%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2025-12-28T22:33:05.565856image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.0004513
Coefficient of variation (CV)0.66560147
Kurtosis-1.250774
Mean3.0054791
Median Absolute Deviation (MAD)2
Skewness-0.004111291
Sum3306027
Variance4.0018054
MonotonicityNot monotonic
2025-12-28T22:33:05.658714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
4157717
14.3%
5157717
14.3%
6157717
14.3%
0156713
14.2%
1156712
14.2%
2156712
14.2%
3156712
14.2%
ValueCountFrequency (%)
0156713
14.2%
1156712
14.2%
2156712
14.2%
3156712
14.2%
4157717
14.3%
5157717
14.3%
6157717
14.3%
ValueCountFrequency (%)
6157717
14.3%
5157717
14.3%
4157717
14.3%
3156712
14.2%
2156712
14.2%
1156712
14.2%
0156713
14.2%

is_weekend
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size52.5 MiB
0
784566 
1
315434 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1100000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0784566
71.3%
1315434
28.7%

Length

2025-12-28T22:33:05.774816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-28T22:33:05.847762image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0784566
71.3%
1315434
28.7%

Most occurring characters

ValueCountFrequency (%)
0784566
71.3%
1315434
28.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1100000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0784566
71.3%
1315434
28.7%

Most occurring scripts

ValueCountFrequency (%)
Common1100000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0784566
71.3%
1315434
28.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII1100000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0784566
71.3%
1315434
28.7%

is_holiday
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size52.5 MiB
0
1084932 
1
 
15068

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1100000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
01084932
98.6%
115068
 
1.4%

Length

2025-12-28T22:33:05.932020image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-28T22:33:06.002153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
01084932
98.6%
115068
 
1.4%

Most occurring characters

ValueCountFrequency (%)
01084932
98.6%
115068
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1100000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01084932
98.6%
115068
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
Common1100000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01084932
98.6%
115068
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII1100000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01084932
98.6%
115068
 
1.4%

temperature
Real number (ℝ)

Distinct710
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.815005
Minimum1.8
Maximum22.83
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2025-12-28T22:33:06.084301image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.8
5-th percentile7.25
Q110.61
median12.84
Q315
95-th percentile18.53
Maximum22.83
Range21.03
Interquartile range (IQR)4.39

Descriptive statistics

Standard deviation3.3715868
Coefficient of variation (CV)0.26309679
Kurtosis-0.048365811
Mean12.815005
Median Absolute Deviation (MAD)2.21
Skewness-0.060420163
Sum14096506
Variance11.367598
MonotonicityNot monotonic
2025-12-28T22:33:06.199360image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.315024
 
0.5%
12.025024
 
0.5%
14.415023
 
0.5%
13.745022
 
0.5%
11.014020
 
0.4%
13.134020
 
0.4%
14.814019
 
0.4%
14.84019
 
0.4%
13.484019
 
0.4%
12.994019
 
0.4%
Other values (700)1055791
96.0%
ValueCountFrequency (%)
1.81005
0.1%
2.741005
0.1%
3.191005
0.1%
3.621004
0.1%
3.881005
0.1%
4.041004
0.1%
4.131005
0.1%
4.272009
0.2%
4.561004
0.1%
4.891004
0.1%
ValueCountFrequency (%)
22.831005
0.1%
22.591004
0.1%
22.31005
0.1%
21.581005
0.1%
21.541005
0.1%
21.281004
0.1%
21.261005
0.1%
20.831004
0.1%
20.61005
0.1%
20.361004
0.1%

rain_mm
Real number (ℝ)

Distinct559
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9041064
Minimum0
Maximum11.58
Zeros1005
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2025-12-28T22:33:06.315570image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.23
Q11.22
median2.57
Q34.15
95-th percentile6.85
Maximum11.58
Range11.58
Interquartile range (IQR)2.93

Descriptive statistics

Standard deviation2.0989968
Coefficient of variation (CV)0.72276859
Kurtosis0.7851252
Mean2.9041064
Median Absolute Deviation (MAD)1.46
Skewness0.91618052
Sum3194517
Variance4.4057877
MonotonicityNot monotonic
2025-12-28T22:33:06.438067image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.066029
 
0.5%
0.696027
 
0.5%
0.036027
 
0.5%
0.896026
 
0.5%
1.225025
 
0.5%
0.715025
 
0.5%
1.775024
 
0.5%
15024
 
0.5%
1.435024
 
0.5%
2.515024
 
0.5%
Other values (549)1045745
95.1%
ValueCountFrequency (%)
01005
 
0.1%
0.012010
 
0.2%
0.021004
 
0.1%
0.036027
0.5%
0.043014
0.3%
0.054018
0.4%
0.062010
 
0.2%
0.073013
0.3%
0.084020
0.4%
0.092008
 
0.2%
ValueCountFrequency (%)
11.581005
0.1%
11.411005
0.1%
11.331004
0.1%
10.961004
0.1%
10.851004
0.1%
10.281005
0.1%
9.931005
0.1%
9.861004
0.1%
9.771005
0.1%
9.281004
0.1%

store_id
Categorical

High correlation 

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size60.8 MiB
STORE0001
87600 
STORE0002
87600 
STORE0003
87600 
STORE0004
87600 
STORE0005
87600 
Other values (8)
662000 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters9900000
Distinct characters15
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSTORE0001
2nd rowSTORE0001
3rd rowSTORE0001
4th rowSTORE0001
5th rowSTORE0001

Common Values

ValueCountFrequency (%)
STORE000187600
 
8.0%
STORE000287600
 
8.0%
STORE000387600
 
8.0%
STORE000487600
 
8.0%
STORE000587600
 
8.0%
STORE000687600
 
8.0%
STORE000787600
 
8.0%
STORE000887600
 
8.0%
STORE000987600
 
8.0%
STORE001087600
 
8.0%
Other values (3)224000
20.4%

Length

2025-12-28T22:33:06.551902image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
store000187600
 
8.0%
store000287600
 
8.0%
store000387600
 
8.0%
store000487600
 
8.0%
store000587600
 
8.0%
store000687600
 
8.0%
store000787600
 
8.0%
store000887600
 
8.0%
store000987600
 
8.0%
store001087600
 
8.0%
Other values (3)224000
20.4%

Most occurring characters

ValueCountFrequency (%)
03076000
31.1%
T1100000
 
11.1%
S1100000
 
11.1%
O1100000
 
11.1%
R1100000
 
11.1%
E1100000
 
11.1%
1486800
 
4.9%
2175200
 
1.8%
3136400
 
1.4%
487600
 
0.9%
Other values (5)438000
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter5500000
55.6%
Decimal Number4400000
44.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03076000
69.9%
1486800
 
11.1%
2175200
 
4.0%
3136400
 
3.1%
487600
 
2.0%
587600
 
2.0%
687600
 
2.0%
787600
 
2.0%
887600
 
2.0%
987600
 
2.0%
Uppercase Letter
ValueCountFrequency (%)
T1100000
20.0%
S1100000
20.0%
O1100000
20.0%
R1100000
20.0%
E1100000
20.0%

Most occurring scripts

ValueCountFrequency (%)
Latin5500000
55.6%
Common4400000
44.4%

Most frequent character per script

Common
ValueCountFrequency (%)
03076000
69.9%
1486800
 
11.1%
2175200
 
4.0%
3136400
 
3.1%
487600
 
2.0%
587600
 
2.0%
687600
 
2.0%
787600
 
2.0%
887600
 
2.0%
987600
 
2.0%
Latin
ValueCountFrequency (%)
T1100000
20.0%
S1100000
20.0%
O1100000
20.0%
R1100000
20.0%
E1100000
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII9900000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03076000
31.1%
T1100000
 
11.1%
S1100000
 
11.1%
O1100000
 
11.1%
R1100000
 
11.1%
E1100000
 
11.1%
1486800
 
4.9%
2175200
 
1.8%
3136400
 
1.4%
487600
 
0.9%
Other values (5)438000
 
4.4%

country
Categorical

High correlation 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size57.6 MiB
Italy
350400 
Spain
262800 
Germany
175200 
Poland
87600 
France
87600 
Other values (2)
136400 

Length

Max length11
Median length5
Mean length5.9032727
Min length5

Characters and Unicode

Total characters6493600
Distinct characters23
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGermany
2nd rowGermany
3rd rowGermany
4th rowGermany
5th rowGermany

Common Values

ValueCountFrequency (%)
Italy350400
31.9%
Spain262800
23.9%
Germany175200
15.9%
Poland87600
 
8.0%
France87600
 
8.0%
Austria87600
 
8.0%
Netherlands48800
 
4.4%

Length

2025-12-28T22:33:06.645986image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-28T22:33:06.726382image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
italy350400
31.9%
spain262800
23.9%
germany175200
15.9%
poland87600
 
8.0%
france87600
 
8.0%
austria87600
 
8.0%
netherlands48800
 
4.4%

Most occurring characters

ValueCountFrequency (%)
a1100000
16.9%
n662000
10.2%
y525600
 
8.1%
l486800
 
7.5%
t486800
 
7.5%
r399200
 
6.1%
e360400
 
5.6%
I350400
 
5.4%
i350400
 
5.4%
p262800
 
4.0%
Other values (13)1509200
23.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter5393600
83.1%
Uppercase Letter1100000
 
16.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a1100000
20.4%
n662000
12.3%
y525600
9.7%
l486800
9.0%
t486800
9.0%
r399200
 
7.4%
e360400
 
6.7%
i350400
 
6.5%
p262800
 
4.9%
m175200
 
3.2%
Other values (6)584400
10.8%
Uppercase Letter
ValueCountFrequency (%)
I350400
31.9%
S262800
23.9%
G175200
15.9%
P87600
 
8.0%
F87600
 
8.0%
A87600
 
8.0%
N48800
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
Latin6493600
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a1100000
16.9%
n662000
10.2%
y525600
 
8.1%
l486800
 
7.5%
t486800
 
7.5%
r399200
 
6.1%
e360400
 
5.6%
I350400
 
5.4%
i350400
 
5.4%
p262800
 
4.0%
Other values (13)1509200
23.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII6493600
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a1100000
16.9%
n662000
10.2%
y525600
 
8.1%
l486800
 
7.5%
t486800
 
7.5%
r399200
 
6.1%
e360400
 
5.6%
I350400
 
5.4%
i350400
 
5.4%
p262800
 
4.0%
Other values (13)1509200
23.2%

city
Categorical

High correlation 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size57.8 MiB
Berlin
175200 
Rome
175200 
Milan
175200 
Barcelona
175200 
Warsaw
87600 
Other values (4)
311600 

Length

Max length9
Median length6
Mean length6.0534545
Min length4

Characters and Unicode

Total characters6658800
Distinct characters20
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBerlin
2nd rowBerlin
3rd rowBerlin
4th rowBerlin
5th rowBerlin

Common Values

ValueCountFrequency (%)
Berlin175200
15.9%
Rome175200
15.9%
Milan175200
15.9%
Barcelona175200
15.9%
Warsaw87600
8.0%
Paris87600
8.0%
Vienna87600
8.0%
Madrid87600
8.0%
Amsterdam48800
 
4.4%

Length

2025-12-28T22:33:06.829112image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-28T22:33:06.914065image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
berlin175200
15.9%
rome175200
15.9%
milan175200
15.9%
barcelona175200
15.9%
warsaw87600
8.0%
paris87600
8.0%
vienna87600
8.0%
madrid87600
8.0%
amsterdam48800
 
4.4%

Most occurring characters

ValueCountFrequency (%)
a1012400
15.2%
n700800
10.5%
e662000
9.9%
r662000
9.9%
i613200
9.2%
l525600
7.9%
B350400
 
5.3%
o350400
 
5.3%
m272800
 
4.1%
M262800
 
3.9%
Other values (10)1246400
18.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter5558800
83.5%
Uppercase Letter1100000
 
16.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a1012400
18.2%
n700800
12.6%
e662000
11.9%
r662000
11.9%
i613200
11.0%
l525600
9.5%
o350400
 
6.3%
m272800
 
4.9%
d224000
 
4.0%
s224000
 
4.0%
Other values (3)311600
 
5.6%
Uppercase Letter
ValueCountFrequency (%)
B350400
31.9%
M262800
23.9%
R175200
15.9%
W87600
 
8.0%
P87600
 
8.0%
V87600
 
8.0%
A48800
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
Latin6658800
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a1012400
15.2%
n700800
10.5%
e662000
9.9%
r662000
9.9%
i613200
9.2%
l525600
7.9%
B350400
 
5.3%
o350400
 
5.3%
m272800
 
4.1%
M262800
 
3.9%
Other values (10)1246400
18.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII6658800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a1012400
15.2%
n700800
10.5%
e662000
9.9%
r662000
9.9%
i613200
9.2%
l525600
7.9%
B350400
 
5.3%
o350400
 
5.3%
m272800
 
4.1%
M262800
 
3.9%
Other values (10)1246400
18.7%

channel
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size62.7 MiB
Hypermarket
525600 
Supermarket
262800 
E-commerce
262800 
Convenience
 
48800

Length

Max length11
Median length11
Mean length10.761091
Min length10

Characters and Unicode

Total characters11837200
Distinct characters19
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHypermarket
2nd rowHypermarket
3rd rowHypermarket
4th rowHypermarket
5th rowHypermarket

Common Values

ValueCountFrequency (%)
Hypermarket525600
47.8%
Supermarket262800
23.9%
E-commerce262800
23.9%
Convenience48800
 
4.4%

Length

2025-12-28T22:33:07.026418image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-28T22:33:07.091190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
hypermarket525600
47.8%
supermarket262800
23.9%
e-commerce262800
23.9%
convenience48800
 
4.4%

Most occurring characters

ValueCountFrequency (%)
e2248800
19.0%
r1839600
15.5%
m1314000
11.1%
k788400
 
6.7%
t788400
 
6.7%
p788400
 
6.7%
a788400
 
6.7%
c574400
 
4.9%
H525600
 
4.4%
y525600
 
4.4%
Other values (9)1655600
14.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter10474400
88.5%
Uppercase Letter1100000
 
9.3%
Dash Punctuation262800
 
2.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e2248800
21.5%
r1839600
17.6%
m1314000
12.5%
k788400
 
7.5%
t788400
 
7.5%
p788400
 
7.5%
a788400
 
7.5%
c574400
 
5.5%
y525600
 
5.0%
o311600
 
3.0%
Other values (4)506800
 
4.8%
Uppercase Letter
ValueCountFrequency (%)
H525600
47.8%
S262800
23.9%
E262800
23.9%
C48800
 
4.4%
Dash Punctuation
ValueCountFrequency (%)
-262800
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin11574400
97.8%
Common262800
 
2.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e2248800
19.4%
r1839600
15.9%
m1314000
11.4%
k788400
 
6.8%
t788400
 
6.8%
p788400
 
6.8%
a788400
 
6.8%
c574400
 
5.0%
H525600
 
4.5%
y525600
 
4.5%
Other values (8)1392800
12.0%
Common
ValueCountFrequency (%)
-262800
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII11837200
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e2248800
19.0%
r1839600
15.5%
m1314000
11.1%
k788400
 
6.7%
t788400
 
6.7%
p788400
 
6.7%
a788400
 
6.7%
c574400
 
4.9%
H525600
 
4.4%
y525600
 
4.4%
Other values (9)1655600
14.0%

latitude
Real number (ℝ)

High correlation 

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.305811
Minimum40.41706
Maximum52.52586
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2025-12-28T22:33:07.168131image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum40.41706
5-th percentile40.41706
Q141.90833
median45.46266
Q352.25287
95-th percentile52.52586
Maximum52.52586
Range12.1088
Interquartile range (IQR)10.34454

Descriptive statistics

Standard deviation4.6029269
Coefficient of variation (CV)0.099402791
Kurtosis-1.555797
Mean46.305811
Median Absolute Deviation (MAD)4.06112
Skewness0.18305392
Sum50936392
Variance21.186936
MonotonicityNot monotonic
2025-12-28T22:33:07.253111image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
52.5258687600
 
8.0%
41.9401287600
 
8.0%
45.4403787600
 
8.0%
52.5005187600
 
8.0%
52.2528787600
 
8.0%
48.8758787600
 
8.0%
41.4015487600
 
8.0%
48.2032987600
 
8.0%
41.3673187600
 
8.0%
45.4626687600
 
8.0%
Other values (3)224000
20.4%
ValueCountFrequency (%)
40.4170687600
8.0%
41.3673187600
8.0%
41.4015487600
8.0%
41.9083387600
8.0%
41.9401287600
8.0%
45.4403787600
8.0%
45.4626687600
8.0%
48.2032987600
8.0%
48.8758787600
8.0%
52.2528787600
8.0%
ValueCountFrequency (%)
52.5258687600
8.0%
52.5005187600
8.0%
52.3623148800
4.4%
52.2528787600
8.0%
48.8758787600
8.0%
48.2032987600
8.0%
45.4626687600
8.0%
45.4403787600
8.0%
41.9401287600
8.0%
41.9083387600
8.0%

longitude
Real number (ℝ)

High correlation 

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.030856
Minimum-3.67473
Maximum20.99579
Zeros0
Zeros (%)0.0%
Negative87600
Negative (%)8.0%
Memory size8.4 MiB
2025-12-28T22:33:07.338279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-3.67473
5-th percentile-3.67473
Q12.36046
median9.20313
Q313.39071
95-th percentile20.99579
Maximum20.99579
Range24.67052
Interquartile range (IQR)11.03025

Descriptive statistics

Standard deviation6.7274441
Coefficient of variation (CV)0.7449398
Kurtosis-0.78421852
Mean9.030856
Median Absolute Deviation (MAD)4.24463
Skewness-0.17507884
Sum9933941.6
Variance45.258504
MonotonicityNot monotonic
2025-12-28T22:33:07.432770image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
13.3907187600
 
8.0%
12.5058887600
 
8.0%
9.2031387600
 
8.0%
13.4207487600
 
8.0%
20.9957987600
 
8.0%
2.3604687600
 
8.0%
2.2113487600
 
8.0%
16.3587387600
 
8.0%
2.1570887600
 
8.0%
9.1968287600
 
8.0%
Other values (3)224000
20.4%
ValueCountFrequency (%)
-3.6747387600
8.0%
2.1570887600
8.0%
2.2113487600
8.0%
2.3604687600
8.0%
4.958548800
4.4%
9.1968287600
8.0%
9.2031387600
8.0%
12.5058887600
8.0%
12.5129487600
8.0%
13.3907187600
8.0%
ValueCountFrequency (%)
20.9957987600
8.0%
16.3587387600
8.0%
13.4207487600
8.0%
13.3907187600
8.0%
12.5129487600
8.0%
12.5058887600
8.0%
9.2031387600
8.0%
9.1968287600
8.0%
4.958548800
4.4%
2.3604687600
8.0%

sku_id
Text

Distinct102
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size58.7 MiB
2025-12-28T22:33:07.695214image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters7700000
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSKU0086
2nd rowSKU0086
3rd rowSKU0086
4th rowSKU0086
5th rowSKU0086
ValueCountFrequency (%)
sku002014235
 
1.3%
sku000114235
 
1.3%
sku009614235
 
1.3%
sku003914235
 
1.3%
sku001513140
 
1.2%
sku000613140
 
1.2%
sku005613140
 
1.2%
sku002813140
 
1.2%
sku009713140
 
1.2%
sku008713140
 
1.2%
Other values (92)964220
87.7%
2025-12-28T22:33:08.064345image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
02416810
31.4%
S1100000
14.3%
K1100000
14.3%
U1100000
14.3%
1256230
 
3.3%
6229950
 
3.0%
8224475
 
2.9%
5222285
 
2.9%
2214620
 
2.8%
3214145
 
2.8%
Other values (3)621485
 
8.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number4400000
57.1%
Uppercase Letter3300000
42.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02416810
54.9%
1256230
 
5.8%
6229950
 
5.2%
8224475
 
5.1%
5222285
 
5.1%
2214620
 
4.9%
3214145
 
4.9%
7209765
 
4.8%
9208050
 
4.7%
4203670
 
4.6%
Uppercase Letter
ValueCountFrequency (%)
S1100000
33.3%
K1100000
33.3%
U1100000
33.3%

Most occurring scripts

ValueCountFrequency (%)
Common4400000
57.1%
Latin3300000
42.9%

Most frequent character per script

Common
ValueCountFrequency (%)
02416810
54.9%
1256230
 
5.8%
6229950
 
5.2%
8224475
 
5.1%
5222285
 
5.1%
2214620
 
4.9%
3214145
 
4.9%
7209765
 
4.8%
9208050
 
4.7%
4203670
 
4.6%
Latin
ValueCountFrequency (%)
S1100000
33.3%
K1100000
33.3%
U1100000
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII7700000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02416810
31.4%
S1100000
14.3%
K1100000
14.3%
U1100000
14.3%
1256230
 
3.3%
6229950
 
3.0%
8224475
 
2.9%
5222285
 
2.9%
2214620
 
2.8%
3214145
 
2.8%
Other values (3)621485
 
8.1%
Distinct102
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size65.7 MiB
2025-12-28T22:33:08.260781image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length19
Median length16
Mean length13.629259
Min length11

Characters and Unicode

Total characters14992185
Distinct characters33
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBrandB Shampoo
2nd rowBrandB Shampoo
3rd rowBrandB Shampoo
4th rowBrandB Shampoo
5th rowBrandB Shampoo
ValueCountFrequency (%)
brandf187245
 
8.3%
brandb186150
 
8.2%
brandd186150
 
8.2%
branda182390
 
8.1%
brandc180675
 
8.0%
brande177390
 
7.8%
soda71175
 
3.1%
toothpaste70080
 
3.1%
shampoo70080
 
3.1%
cheese68985
 
3.0%
Other values (14)882095
39.0%
2025-12-28T22:33:08.552969image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a1556615
 
10.4%
r1527670
 
10.2%
n1401745
 
9.3%
B1354040
 
9.0%
d1233590
 
8.2%
1162415
 
7.8%
e949510
 
6.3%
o662000
 
4.4%
t642290
 
4.3%
C435810
 
2.9%
Other values (23)4066500
27.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter10529770
70.2%
Uppercase Letter3300000
 
22.0%
Space Separator1162415
 
7.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a1556615
14.8%
r1527670
14.5%
n1401745
13.3%
d1233590
11.7%
e949510
9.0%
o662000
6.3%
t642290
6.1%
s404055
 
3.8%
i398580
 
3.8%
h340545
 
3.2%
Other values (9)1413170
13.4%
Uppercase Letter
ValueCountFrequency (%)
B1354040
41.0%
C435810
 
13.2%
S263420
 
8.0%
D245280
 
7.4%
E239805
 
7.3%
F187245
 
5.7%
A182390
 
5.5%
T70080
 
2.1%
M67890
 
2.1%
W66795
 
2.0%
Other values (3)187245
 
5.7%
Space Separator
ValueCountFrequency (%)
1162415
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin13829770
92.2%
Common1162415
 
7.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a1556615
11.3%
r1527670
11.0%
n1401745
 
10.1%
B1354040
 
9.8%
d1233590
 
8.9%
e949510
 
6.9%
o662000
 
4.8%
t642290
 
4.6%
C435810
 
3.2%
s404055
 
2.9%
Other values (22)3662445
26.5%
Common
ValueCountFrequency (%)
1162415
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII14992185
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a1556615
 
10.4%
r1527670
 
10.2%
n1401745
 
9.3%
B1354040
 
9.0%
d1233590
 
8.2%
1162415
 
7.8%
e949510
 
6.3%
o662000
 
4.4%
t642290
 
4.3%
C435810
 
2.9%
Other values (23)4066500
27.1%

category
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size60.1 MiB
Beverages
266085 
Snacks
261705 
Personal Care
199290 
Dairy
196005 
Home Care
176915 

Length

Max length13
Median length9
Mean length8.2982045
Min length5

Characters and Unicode

Total characters9128025
Distinct characters21
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPersonal Care
2nd rowPersonal Care
3rd rowPersonal Care
4th rowPersonal Care
5th rowPersonal Care

Common Values

ValueCountFrequency (%)
Beverages266085
24.2%
Snacks261705
23.8%
Personal Care199290
18.1%
Dairy196005
17.8%
Home Care176915
16.1%

Length

2025-12-28T22:33:08.668257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-28T22:33:08.756674image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
care376205
25.5%
beverages266085
18.0%
snacks261705
17.7%
personal199290
13.5%
dairy196005
13.3%
home176915
12.0%

Most occurring characters

ValueCountFrequency (%)
e1550665
17.0%
a1299290
14.2%
r1037585
11.4%
s727080
 
8.0%
n460995
 
5.1%
C376205
 
4.1%
376205
 
4.1%
o376205
 
4.1%
B266085
 
2.9%
v266085
 
2.9%
Other values (11)2391625
26.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter7275615
79.7%
Uppercase Letter1476205
 
16.2%
Space Separator376205
 
4.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e1550665
21.3%
a1299290
17.9%
r1037585
14.3%
s727080
10.0%
n460995
 
6.3%
o376205
 
5.2%
v266085
 
3.7%
g266085
 
3.7%
c261705
 
3.6%
k261705
 
3.6%
Other values (4)768215
10.6%
Uppercase Letter
ValueCountFrequency (%)
C376205
25.5%
B266085
18.0%
S261705
17.7%
P199290
13.5%
D196005
13.3%
H176915
12.0%
Space Separator
ValueCountFrequency (%)
376205
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin8751820
95.9%
Common376205
 
4.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e1550665
17.7%
a1299290
14.8%
r1037585
11.9%
s727080
 
8.3%
n460995
 
5.3%
C376205
 
4.3%
o376205
 
4.3%
B266085
 
3.0%
v266085
 
3.0%
g266085
 
3.0%
Other values (10)2125540
24.3%
Common
ValueCountFrequency (%)
376205
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII9128025
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e1550665
17.0%
a1299290
14.2%
r1037585
11.4%
s727080
 
8.0%
n460995
 
5.1%
C376205
 
4.1%
376205
 
4.1%
o376205
 
4.1%
B266085
 
2.9%
v266085
 
2.9%
Other values (11)2391625
26.2%

subcategory
Categorical

High correlation 

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size58.4 MiB
Soda
 
71175
Shampoo
 
70080
Toothpaste
 
70080
Cheese
 
68985
Milk
 
67890
Other values (12)
751790 

Length

Max length12
Median length9
Mean length6.6292591
Min length4

Characters and Unicode

Total characters7292185
Distinct characters31
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowShampoo
2nd rowShampoo
3rd rowShampoo
4th rowShampoo
5th rowShampoo

Common Values

ValueCountFrequency (%)
Soda71175
 
6.5%
Shampoo70080
 
6.4%
Toothpaste70080
 
6.4%
Cheese68985
 
6.3%
Milk67890
 
6.2%
Biscuits67890
 
6.2%
Chips66795
 
6.1%
Water66795
 
6.1%
Juice65700
 
6.0%
Chocolate64605
 
5.9%
Other values (7)420005
38.2%

Length

2025-12-28T22:33:08.875119image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
soda71175
 
6.1%
shampoo70080
 
6.0%
toothpaste70080
 
6.0%
cheese68985
 
5.9%
milk67890
 
5.8%
biscuits67890
 
5.8%
chips66795
 
5.7%
water66795
 
5.7%
juice65700
 
5.7%
chocolate64605
 
5.6%
Other values (8)482420
41.5%

Most occurring characters

ValueCountFrequency (%)
e949510
 
13.0%
o662000
 
9.1%
t642290
 
8.8%
a456615
 
6.3%
r427670
 
5.9%
s404055
 
5.5%
i398580
 
5.5%
h340545
 
4.7%
n301745
 
4.1%
p266085
 
3.6%
Other values (21)2443090
33.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter6129770
84.1%
Uppercase Letter1100000
 
15.1%
Space Separator62415
 
0.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e949510
15.5%
o662000
10.8%
t642290
10.5%
a456615
 
7.4%
r427670
 
7.0%
s404055
 
6.6%
i398580
 
6.5%
h340545
 
5.6%
n301745
 
4.9%
p266085
 
4.3%
Other values (9)1280675
20.9%
Uppercase Letter
ValueCountFrequency (%)
S263420
23.9%
C255135
23.2%
T70080
 
6.4%
M67890
 
6.2%
B67890
 
6.2%
W66795
 
6.1%
J65700
 
6.0%
N62415
 
5.7%
E62415
 
5.7%
D59130
 
5.4%
Space Separator
ValueCountFrequency (%)
62415
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin7229770
99.1%
Common62415
 
0.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e949510
 
13.1%
o662000
 
9.2%
t642290
 
8.9%
a456615
 
6.3%
r427670
 
5.9%
s404055
 
5.6%
i398580
 
5.5%
h340545
 
4.7%
n301745
 
4.2%
p266085
 
3.7%
Other values (20)2380675
32.9%
Common
ValueCountFrequency (%)
62415
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII7292185
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e949510
 
13.0%
o662000
 
9.1%
t642290
 
8.8%
a456615
 
6.3%
r427670
 
5.9%
s404055
 
5.5%
i398580
 
5.5%
h340545
 
4.7%
n301745
 
4.1%
p266085
 
3.6%
Other values (21)2443090
33.5%

brand
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size57.7 MiB
BrandF
187245 
BrandB
186150 
BrandD
186150 
BrandA
182390 
BrandC
180675 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters6600000
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBrandB
2nd rowBrandB
3rd rowBrandB
4th rowBrandB
5th rowBrandB

Common Values

ValueCountFrequency (%)
BrandF187245
17.0%
BrandB186150
16.9%
BrandD186150
16.9%
BrandA182390
16.6%
BrandC180675
16.4%
BrandE177390
16.1%

Length

2025-12-28T22:33:08.980188image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-28T22:33:09.059357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
brandf187245
17.0%
brandb186150
16.9%
brandd186150
16.9%
branda182390
16.6%
brandc180675
16.4%
brande177390
16.1%

Most occurring characters

ValueCountFrequency (%)
B1286150
19.5%
r1100000
16.7%
a1100000
16.7%
n1100000
16.7%
d1100000
16.7%
F187245
 
2.8%
D186150
 
2.8%
A182390
 
2.8%
C180675
 
2.7%
E177390
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4400000
66.7%
Uppercase Letter2200000
33.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B1286150
58.5%
F187245
 
8.5%
D186150
 
8.5%
A182390
 
8.3%
C180675
 
8.2%
E177390
 
8.1%
Lowercase Letter
ValueCountFrequency (%)
r1100000
25.0%
a1100000
25.0%
n1100000
25.0%
d1100000
25.0%

Most occurring scripts

ValueCountFrequency (%)
Latin6600000
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
B1286150
19.5%
r1100000
16.7%
a1100000
16.7%
n1100000
16.7%
d1100000
16.7%
F187245
 
2.8%
D186150
 
2.8%
A182390
 
2.8%
C180675
 
2.7%
E177390
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII6600000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B1286150
19.5%
r1100000
16.7%
a1100000
16.7%
n1100000
16.7%
d1100000
16.7%
F187245
 
2.8%
D186150
 
2.8%
A182390
 
2.8%
C180675
 
2.7%
E177390
 
2.7%

units_sold
Real number (ℝ)

High correlation 

Distinct516
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59.196347
Minimum0
Maximum704
Zeros3092
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2025-12-28T22:33:09.176522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10
Q125
median49
Q382
95-th percentile142
Maximum704
Range704
Interquartile range (IQR)57

Descriptive statistics

Standard deviation45.007217
Coefficient of variation (CV)0.76030395
Kurtosis5.3705509
Mean59.196347
Median Absolute Deviation (MAD)27
Skewness1.6714468
Sum65115982
Variance2025.6496
MonotonicityNot monotonic
2025-12-28T22:33:09.291668image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1815187
 
1.4%
1714936
 
1.4%
2014911
 
1.4%
1914851
 
1.4%
2114810
 
1.3%
2314524
 
1.3%
2214477
 
1.3%
1614435
 
1.3%
1514417
 
1.3%
2414221
 
1.3%
Other values (506)953231
86.7%
ValueCountFrequency (%)
03092
 
0.3%
12840
 
0.3%
22878
 
0.3%
33241
 
0.3%
44012
0.4%
55097
0.5%
66190
0.6%
77693
0.7%
88912
0.8%
99843
0.9%
ValueCountFrequency (%)
7041
< 0.1%
6911
< 0.1%
6801
< 0.1%
6271
< 0.1%
6261
< 0.1%
6011
< 0.1%
5821
< 0.1%
5801
< 0.1%
5771
< 0.1%
5761
< 0.1%

list_price
Real number (ℝ)

High correlation 

Distinct99
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.7121003
Minimum1.08
Maximum14.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2025-12-28T22:33:09.407724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.08
5-th percentile1.44
Q14.2
median7.38
Q311.65
95-th percentile14.15
Maximum14.8
Range13.72
Interquartile range (IQR)7.45

Descriptive statistics

Standard deviation4.2530229
Coefficient of variation (CV)0.55147401
Kurtosis-1.3109158
Mean7.7121003
Median Absolute Deviation (MAD)3.78
Skewness0.057888721
Sum8483310.3
Variance18.088203
MonotonicityNot monotonic
2025-12-28T22:33:09.531823image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.2622995
 
2.1%
9.8720805
 
1.9%
13.4220805
 
1.9%
11.814235
 
1.3%
10.5814235
 
1.3%
6.2414235
 
1.3%
2.314235
 
1.3%
9.2913140
 
1.2%
8.1713140
 
1.2%
9.3713140
 
1.2%
Other values (89)939035
85.4%
ValueCountFrequency (%)
1.0812045
1.1%
1.113140
1.2%
1.2913140
1.2%
1.3610950
1.0%
1.449855
0.9%
1.489855
0.9%
1.5710950
1.0%
1.6310950
1.0%
1.7210950
1.0%
1.8110950
1.0%
ValueCountFrequency (%)
14.89855
0.9%
14.5710950
1.0%
14.5212045
1.1%
14.479855
0.9%
14.210950
1.0%
14.1510950
1.0%
14.1110950
1.0%
14.028760
0.8%
13.959855
0.9%
13.7212045
1.1%

discount_pct
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size54.6 MiB
0.0
1011743 
0.15
 
22367
0.1
 
22274
0.2
 
22086
0.3
 
21530

Length

Max length4
Median length3
Mean length3.0203336
Min length3

Characters and Unicode

Total characters3322367
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.1
2nd row0.0
3rd row0.3
4th row0.0
5th row0.2

Common Values

ValueCountFrequency (%)
0.01011743
92.0%
0.1522367
 
2.0%
0.122274
 
2.0%
0.222086
 
2.0%
0.321530
 
2.0%

Length

2025-12-28T22:33:09.664638image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-28T22:33:10.016116image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.01011743
92.0%
0.1522367
 
2.0%
0.122274
 
2.0%
0.222086
 
2.0%
0.321530
 
2.0%

Most occurring characters

ValueCountFrequency (%)
02111743
63.6%
.1100000
33.1%
144641
 
1.3%
522367
 
0.7%
222086
 
0.7%
321530
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2222367
66.9%
Other Punctuation1100000
33.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02111743
95.0%
144641
 
2.0%
522367
 
1.0%
222086
 
1.0%
321530
 
1.0%
Other Punctuation
ValueCountFrequency (%)
.1100000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3322367
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02111743
63.6%
.1100000
33.1%
144641
 
1.3%
522367
 
0.7%
222086
 
0.7%
321530
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII3322367
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02111743
63.6%
.1100000
33.1%
144641
 
1.3%
522367
 
0.7%
222086
 
0.7%
321530
 
0.6%

promo_flag
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size52.5 MiB
0
1011743 
1
 
88257

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1100000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
01011743
92.0%
188257
 
8.0%

Length

2025-12-28T22:33:10.117857image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-28T22:33:10.193248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
01011743
92.0%
188257
 
8.0%

Most occurring characters

ValueCountFrequency (%)
01011743
92.0%
188257
 
8.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1100000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01011743
92.0%
188257
 
8.0%

Most occurring scripts

ValueCountFrequency (%)
Common1100000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01011743
92.0%
188257
 
8.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1100000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01011743
92.0%
188257
 
8.0%

gross_sales
Real number (ℝ)

High correlation 

Distinct15354
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean440.68061
Minimum0
Maximum6593.9
Zeros3092
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2025-12-28T22:33:10.287423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile44.91
Q1132.44
median282.88
Q3605.28
95-th percentile1344.25
Maximum6593.9
Range6593.9
Interquartile range (IQR)472.84

Descriptive statistics

Standard deviation441.80051
Coefficient of variation (CV)1.0025413
Kurtosis5.9618175
Mean440.68061
Median Absolute Deviation (MAD)186.17
Skewness2.0096014
Sum4.8474867 × 108
Variance195187.69
MonotonicityNot monotonic
2025-12-28T22:33:10.419672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03092
 
0.3%
200.61384
 
0.1%
83.921374
 
0.1%
73.431350
 
0.1%
62.941217
 
0.1%
94.411203
 
0.1%
67.21182
 
0.1%
46.21158
 
0.1%
102.191116
 
0.1%
92.91098
 
0.1%
Other values (15344)1085826
98.7%
ValueCountFrequency (%)
03092
0.3%
1.0816
 
< 0.1%
1.121
 
< 0.1%
1.299
 
< 0.1%
1.3669
 
< 0.1%
1.4412
 
< 0.1%
1.4811
 
< 0.1%
1.5714
 
< 0.1%
1.6327
 
< 0.1%
1.7229
 
< 0.1%
ValueCountFrequency (%)
6593.91
< 0.1%
5843.951
< 0.1%
5716.61
< 0.1%
56601
< 0.1%
5617.551
< 0.1%
5575.11
< 0.1%
5532.651
< 0.1%
5461.91
< 0.1%
5447.752
< 0.1%
53771
< 0.1%

net_sales
Real number (ℝ)

High correlation 

Distinct29938
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean429.95132
Minimum0
Maximum5144.94
Zeros3092
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2025-12-28T22:33:10.559605image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile44.88
Q1130.7675
median277.86
Q3593.46
95-th percentile1307.25
Maximum5144.94
Range5144.94
Interquartile range (IQR)462.6925

Descriptive statistics

Standard deviation422.4992
Coefficient of variation (CV)0.98266753
Kurtosis4.2226402
Mean429.95132
Median Absolute Deviation (MAD)181.72
Skewness1.819917
Sum4.7294645 × 108
Variance178505.57
MonotonicityNot monotonic
2025-12-28T22:33:10.690144image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03092
 
0.3%
200.61384
 
0.1%
83.921307
 
0.1%
73.431293
 
0.1%
62.941174
 
0.1%
67.21164
 
0.1%
46.21150
 
0.1%
94.411124
 
0.1%
102.191116
 
0.1%
92.91108
 
0.1%
Other values (29928)1086088
98.7%
ValueCountFrequency (%)
03092
0.3%
0.771
 
< 0.1%
0.881
 
< 0.1%
1.0816
 
< 0.1%
1.119
 
< 0.1%
1.161
 
< 0.1%
1.298
 
< 0.1%
1.341
 
< 0.1%
1.3669
 
< 0.1%
1.4412
 
< 0.1%
ValueCountFrequency (%)
5144.941
< 0.1%
48111
< 0.1%
4630.591
< 0.1%
4615.731
< 0.1%
4584.61
< 0.1%
4495.461
< 0.1%
4494.041
< 0.1%
4486.681
< 0.1%
4471.41
< 0.1%
4460.081
< 0.1%

stock_on_hand
Real number (ℝ)

Distinct640
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean299.47566
Minimum0
Maximum698
Zeros108
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2025-12-28T22:33:10.818737image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile168
Q1245
median300
Q3354
95-th percentile431
Maximum698
Range698
Interquartile range (IQR)109

Descriptive statistics

Standard deviation80.072923
Coefficient of variation (CV)0.26737706
Kurtosis-0.011136862
Mean299.47566
Median Absolute Deviation (MAD)54
Skewness-0.00089299311
Sum3.2942323 × 108
Variance6411.673
MonotonicityNot monotonic
2025-12-28T22:33:10.954497image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3065608
 
0.5%
2925562
 
0.5%
3105554
 
0.5%
2935546
 
0.5%
3055541
 
0.5%
3035532
 
0.5%
3115521
 
0.5%
2955519
 
0.5%
3045510
 
0.5%
3025499
 
0.5%
Other values (630)1044608
95.0%
ValueCountFrequency (%)
0108
< 0.1%
17
 
< 0.1%
24
 
< 0.1%
34
 
< 0.1%
45
 
< 0.1%
57
 
< 0.1%
66
 
< 0.1%
79
 
< 0.1%
87
 
< 0.1%
910
 
< 0.1%
ValueCountFrequency (%)
6981
< 0.1%
6871
< 0.1%
6721
< 0.1%
6701
< 0.1%
6641
< 0.1%
6591
< 0.1%
6491
< 0.1%
6471
< 0.1%
6421
< 0.1%
6411
< 0.1%

stock_out_flag
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size52.5 MiB
0
1066886 
1
 
33114

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1100000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
01066886
97.0%
133114
 
3.0%

Length

2025-12-28T22:33:11.114792image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-28T22:33:11.206614image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
01066886
97.0%
133114
 
3.0%

Most occurring characters

ValueCountFrequency (%)
01066886
97.0%
133114
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1100000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01066886
97.0%
133114
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Common1100000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01066886
97.0%
133114
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1100000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01066886
97.0%
133114
 
3.0%

lead_time_days
Real number (ℝ)

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5004036
Minimum1
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2025-12-28T22:33:11.291378image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q15
median6
Q38
95-th percentile10
Maximum17
Range16
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.0140646
Coefficient of variation (CV)0.30983685
Kurtosis-0.064369839
Mean6.5004036
Median Absolute Deviation (MAD)1
Skewness0.016007546
Sum7150444
Variance4.0564563
MonotonicityNot monotonic
2025-12-28T22:33:11.411811image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
7211021
19.2%
6210825
19.2%
5165291
15.0%
8164959
15.0%
9100678
9.2%
4100437
9.1%
1048325
 
4.4%
348237
 
4.4%
218332
 
1.7%
1118307
 
1.7%
Other values (7)13588
 
1.2%
ValueCountFrequency (%)
16942
 
0.6%
218332
 
1.7%
348237
 
4.4%
4100437
9.1%
5165291
15.0%
6210825
19.2%
7211021
19.2%
8164959
15.0%
9100678
9.2%
1048325
 
4.4%
ValueCountFrequency (%)
171
 
< 0.1%
163
 
< 0.1%
1535
 
< 0.1%
14195
 
< 0.1%
131162
 
0.1%
125250
 
0.5%
1118307
 
1.7%
1048325
 
4.4%
9100678
9.2%
8164959
15.0%
Distinct60
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size55.6 MiB
2025-12-28T22:33:11.667655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters4400000
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowS008
2nd rowS057
3rd rowS017
4th rowS012
5th rowS038
ValueCountFrequency (%)
s03718579
 
1.7%
s05118572
 
1.7%
s04118533
 
1.7%
s04018529
 
1.7%
s02018507
 
1.7%
s01918502
 
1.7%
s00718481
 
1.7%
s06018474
 
1.7%
s02818469
 
1.7%
s02418469
 
1.7%
Other values (50)914885
83.2%
2025-12-28T22:33:12.018415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
01375714
31.3%
S1100000
25.0%
4293847
 
6.7%
1293284
 
6.7%
2293150
 
6.7%
5292959
 
6.7%
3292837
 
6.7%
6128062
 
2.9%
7110414
 
2.5%
9110056
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3300000
75.0%
Uppercase Letter1100000
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01375714
41.7%
4293847
 
8.9%
1293284
 
8.9%
2293150
 
8.9%
5292959
 
8.9%
3292837
 
8.9%
6128062
 
3.9%
7110414
 
3.3%
9110056
 
3.3%
8109677
 
3.3%
Uppercase Letter
ValueCountFrequency (%)
S1100000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3300000
75.0%
Latin1100000
 
25.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01375714
41.7%
4293847
 
8.9%
1293284
 
8.9%
2293150
 
8.9%
5292959
 
8.9%
3292837
 
8.9%
6128062
 
3.9%
7110414
 
3.3%
9110056
 
3.3%
8109677
 
3.3%
Latin
ValueCountFrequency (%)
S1100000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII4400000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01375714
31.3%
S1100000
25.0%
4293847
 
6.7%
1293284
 
6.7%
2293150
 
6.7%
5292959
 
6.7%
3292837
 
6.7%
6128062
 
2.9%
7110414
 
2.5%
9110056
 
2.5%

purchase_cost
Real number (ℝ)

High correlation 

Distinct1062
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.6255461
Minimum0.49
Maximum11.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2025-12-28T22:33:12.152467image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.49
5-th percentile0.84
Q12.4
median4.35
Q36.77
95-th percentile9.18
Maximum11.1
Range10.61
Interquartile range (IQR)4.37

Descriptive statistics

Standard deviation2.6626044
Coefficient of variation (CV)0.57563028
Kurtosis-1.0081105
Mean4.6255461
Median Absolute Deviation (MAD)2.21
Skewness0.25392754
Sum5088100.7
Variance7.0894621
MonotonicityNot monotonic
2025-12-28T22:33:12.285579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.782470
 
0.2%
0.82440
 
0.2%
0.792439
 
0.2%
0.822362
 
0.2%
0.772337
 
0.2%
0.812335
 
0.2%
0.952324
 
0.2%
0.962301
 
0.2%
0.762296
 
0.2%
0.942289
 
0.2%
Other values (1052)1076407
97.9%
ValueCountFrequency (%)
0.49302
 
< 0.1%
0.5772
0.1%
0.51782
0.1%
0.52754
0.1%
0.53806
0.1%
0.54796
0.1%
0.55814
0.1%
0.56776
0.1%
0.57752
0.1%
0.58893
0.1%
ValueCountFrequency (%)
11.114
< 0.1%
11.0923
< 0.1%
11.0823
< 0.1%
11.0726
< 0.1%
11.0622
< 0.1%
11.0518
< 0.1%
11.0426
< 0.1%
11.0321
< 0.1%
11.0226
< 0.1%
11.0127
< 0.1%

margin_pct
Real number (ℝ)

High correlation 

Distinct601
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.38524047
Minimum-0.05
Maximum0.55
Zeros75
Zeros (%)< 0.1%
Negative3570
Negative (%)0.3%
Memory size8.4 MiB
2025-12-28T22:33:12.403799image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-0.05
5-th percentile0.25
Q10.312
median0.389
Q30.469
95-th percentile0.534
Maximum0.55
Range0.6
Interquartile range (IQR)0.157

Descriptive statistics

Standard deviation0.10245806
Coefficient of variation (CV)0.26595872
Kurtosis0.61135897
Mean0.38524047
Median Absolute Deviation (MAD)0.079
Skewness-0.59936561
Sum423764.51
Variance0.010497654
MonotonicityNot monotonic
2025-12-28T22:33:12.529619image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3133719
 
0.3%
0.3433705
 
0.3%
0.2553705
 
0.3%
0.2613703
 
0.3%
0.3473696
 
0.3%
0.2973682
 
0.3%
0.2833678
 
0.3%
0.3363674
 
0.3%
0.3443672
 
0.3%
0.333669
 
0.3%
Other values (591)1063097
96.6%
ValueCountFrequency (%)
-0.0532
 
< 0.1%
-0.04971
< 0.1%
-0.04857
< 0.1%
-0.04772
< 0.1%
-0.04662
< 0.1%
-0.04575
< 0.1%
-0.04484
< 0.1%
-0.04383
< 0.1%
-0.04284
< 0.1%
-0.04175
< 0.1%
ValueCountFrequency (%)
0.551678
0.2%
0.5493336
0.3%
0.5483373
0.3%
0.5473409
0.3%
0.5463415
0.3%
0.5453292
0.3%
0.5443309
0.3%
0.5433386
0.3%
0.5423394
0.3%
0.5413397
0.3%

Interactions

2025-12-28T22:32:51.057137image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-28T22:31:52.069077image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-28T22:31:55.613122image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-28T22:31:59.066218image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-28T22:32:02.610362image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-28T22:32:06.030825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-28T22:32:09.611794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-28T22:32:13.054369image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-28T22:32:17.065456image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-28T22:32:23.177094image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-28T22:32:27.225858image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-28T22:32:30.987052image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-28T22:32:35.055476image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-28T22:32:38.839507image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-28T22:32:43.033640image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-28T22:32:46.995445image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-28T22:32:51.295074image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-28T22:31:52.265990image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-28T22:31:55.812106image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-28T22:31:59.279745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-28T22:32:02.823805image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-28T22:32:06.242631image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-28T22:32:09.817715image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-28T22:32:13.247931image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-28T22:32:17.507818image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-28T22:32:23.433422image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-28T22:32:27.448045image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-28T22:32:31.236263image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-28T22:32:35.285213image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-28T22:32:39.083535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-28T22:32:43.291798image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-28T22:32:47.223471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-28T22:32:51.531290image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-28T22:31:52.472368image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-28T22:31:56.007232image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-28T22:31:59.479891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-28T22:32:03.028520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-28T22:32:06.464116image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-28T22:32:10.023447image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-28T22:32:13.448484image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-28T22:32:17.849814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-28T22:32:23.679158image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-28T22:32:27.660816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-28T22:32:31.512276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-28T22:32:35.517938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-28T22:32:39.346043image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-28T22:32:43.557184image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-28T22:32:47.467048image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-28T22:32:51.774566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-28T22:31:52.681165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-28T22:31:56.226690image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-28T22:31:59.675962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-28T22:32:03.236274image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-28T22:32:06.666059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-28T22:32:10.225904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-28T22:32:13.643734image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-28T22:32:18.134302image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-28T22:32:23.922120image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-28T22:32:27.872958image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-28T22:32:31.759620image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-28T22:32:35.752670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-28T22:32:39.594322image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-28T22:32:43.807384image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-28T22:32:47.732456image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-12-28T22:32:15.552710image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-28T22:32:21.711704image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-28T22:32:26.061342image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-28T22:32:29.677869image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-12-28T22:32:22.913876image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-12-28T22:32:30.736625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-28T22:32:34.812951image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-28T22:32:38.589921image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-28T22:32:42.783216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-28T22:32:46.764492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-28T22:32:50.806113image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-12-28T22:33:12.677038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
brandcategorychannelcitycountrydaydiscount_pctgross_salesis_holidayis_weekendlatitudelead_time_dayslist_pricelongitudemargin_pctmonthnet_salespromo_flagpurchase_costrain_mmstock_on_handstock_out_flagstore_idsubcategorytemperatureunits_soldweekdayweekofyearyear
brand1.0000.0270.0450.0510.0440.0000.0340.0710.0000.0000.0380.0000.3110.0460.0230.0000.0740.0670.1540.0000.0000.0030.0600.0580.0000.0640.0000.0000.000
category0.0271.0000.0300.0440.0400.0000.0280.0720.0000.0000.0290.0000.3070.0390.0220.0000.0830.0560.1410.0000.0010.0000.0531.0000.0000.0740.0000.0000.000
channel0.0450.0301.0000.8330.7850.0000.0040.0830.0000.0000.5920.0010.0510.7610.0030.0000.0880.0080.0340.0000.0000.0001.0000.0710.0000.1140.0000.0000.000
city0.0510.0440.8331.0001.0000.0000.0050.0450.0000.0001.0000.0000.0471.0000.0030.0000.0480.0110.0280.0000.0010.0011.0000.0670.0000.0580.0000.0000.000
country0.0440.0400.7851.0001.0000.0000.0050.0500.0000.0000.8660.0000.0480.8530.0030.0000.0530.0110.0310.0000.0020.0001.0000.0720.0000.0640.0000.0000.000
day0.0000.0000.0000.0000.0001.0000.0070.0000.1410.032-0.0000.0000.0000.0000.0030.0120.0000.004-0.0000.0270.0020.0000.0000.0000.4520.000-0.0020.0650.000
discount_pct0.0340.0280.0040.0050.0050.0071.0000.0900.0060.0060.0040.0000.0690.0050.4780.0090.0451.0000.0370.0070.0010.0020.0080.0620.0090.1680.0060.0090.006
gross_sales0.0710.0720.0830.0450.0500.0000.0901.0000.0080.0750.044-0.0010.6280.071-0.0530.0130.9980.1730.607-0.001-0.0000.0930.0490.123-0.0040.6910.0590.0140.000
is_holiday0.0000.0000.0000.0000.0000.1410.0060.0081.0000.0640.0000.0020.0000.0000.0040.1260.0080.0050.0000.0910.0000.0000.0000.0000.0760.0090.0990.2140.000
is_weekend0.0000.0000.0000.0000.0000.0320.0060.0750.0641.0000.0000.0000.0000.0000.0020.0380.0820.0000.0000.1100.0000.0000.0000.0000.0600.1041.0000.0060.003
latitude0.0380.0290.5921.0000.866-0.0000.0040.0440.0000.0001.0000.0010.0090.683-0.003-0.0000.0440.0080.007-0.000-0.0000.0011.0000.0590.0000.049-0.000-0.0000.000
lead_time_days0.0000.0000.0010.0000.0000.0000.000-0.0010.0020.0000.0011.000-0.001-0.000-0.000-0.000-0.0010.002-0.0010.001-0.0010.0000.0000.0000.0010.000-0.001-0.0010.000
list_price0.3110.3070.0510.0470.0480.0000.0690.6280.0000.0000.009-0.0011.0000.0040.0170.0000.6340.1360.9640.0000.0010.0000.0510.380-0.000-0.0620.0000.0000.000
longitude0.0460.0390.7611.0000.8530.0000.0050.0710.0000.0000.683-0.0000.0041.000-0.0010.0000.0710.0110.0050.0000.0000.0001.0000.068-0.0000.0820.0000.0000.000
margin_pct0.0230.0220.0030.0030.0030.0030.478-0.0530.0040.002-0.003-0.0000.017-0.0011.0000.001-0.0320.769-0.1960.000-0.0000.0000.0040.0320.002-0.078-0.0010.0010.002
month0.0000.0000.0000.0000.0000.0120.0090.0130.1260.038-0.000-0.0000.0000.0000.0011.0000.0140.0110.0000.043-0.0010.0030.0000.0000.0000.0180.0020.9660.000
net_sales0.0740.0830.0880.0480.0530.0000.0450.9980.0080.0820.044-0.0010.6340.071-0.0320.0141.0000.0880.613-0.001-0.0000.1090.0520.133-0.0030.6830.0600.0140.000
promo_flag0.0670.0560.0080.0110.0110.0041.0000.1730.0050.0000.0080.0020.1360.0110.7690.0110.0881.0000.0730.0050.0000.0010.0170.1230.0140.3250.0020.0070.005
purchase_cost0.1540.1410.0340.0280.031-0.0000.0370.6070.0000.0000.007-0.0010.9640.005-0.1960.0000.6130.0731.0000.0000.0010.0000.0320.189-0.000-0.0680.0000.0000.000
rain_mm0.0000.0000.0000.0000.0000.0270.007-0.0010.0910.110-0.0000.0010.0000.0000.0000.043-0.0010.0050.0001.0000.0000.0000.0000.0000.006-0.001-0.0110.0300.050
stock_on_hand0.0000.0010.0000.0010.0020.0020.001-0.0000.0000.000-0.000-0.0010.0010.000-0.000-0.001-0.0000.0000.0010.0001.0000.0000.0010.0000.001-0.001-0.000-0.0010.000
stock_out_flag0.0030.0000.0000.0010.0000.0000.0020.0930.0000.0000.0010.0000.0000.0000.0000.0030.1090.0010.0000.0000.0001.0000.0000.0010.0000.1210.0020.0020.000
store_id0.0600.0531.0001.0001.0000.0000.0080.0490.0000.0001.0000.0000.0511.0000.0040.0000.0520.0170.0320.0000.0010.0001.0000.0660.0000.0660.0000.0000.000
subcategory0.0581.0000.0710.0670.0720.0000.0620.1230.0000.0000.0590.0000.3800.0680.0320.0000.1330.1230.1890.0000.0000.0010.0661.0000.0000.1080.0000.0000.000
temperature0.0000.0000.0000.0000.0000.4520.009-0.0040.0760.0600.0000.001-0.000-0.0000.0020.000-0.0030.014-0.0000.0060.0010.0000.0000.0001.000-0.005-0.0410.0340.080
units_sold0.0640.0740.1140.0580.0640.0000.1680.6910.0090.1040.0490.000-0.0620.082-0.0780.0180.6830.325-0.068-0.001-0.0010.1210.0660.108-0.0051.0000.0780.0190.002
weekday0.0000.0000.0000.0000.000-0.0020.0060.0590.0991.000-0.000-0.0010.0000.000-0.0010.0020.0600.0020.000-0.011-0.0000.0020.0000.000-0.0410.0781.0000.0050.003
weekofyear0.0000.0000.0000.0000.0000.0650.0090.0140.2140.006-0.000-0.0010.0000.0000.0010.9660.0140.0070.0000.030-0.0010.0020.0000.0000.0340.0190.0051.0000.000
year0.0000.0000.0000.0000.0000.0000.0060.0000.0000.0030.0000.0000.0000.0000.0020.0000.0000.0050.0000.0500.0000.0000.0000.0000.0800.0020.0030.0001.000

Missing values

2025-12-28T22:32:55.702852image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-12-28T22:32:59.093500image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

dateyearmonthdayweekofyearweekdayis_weekendis_holidaytemperaturerain_mmstore_idcountrycitychannellatitudelongitudesku_idsku_namecategorysubcategorybrandunits_soldlist_pricediscount_pctpromo_flaggross_salesnet_salesstock_on_handstock_out_flaglead_time_dayssupplier_idpurchase_costmargin_pct
02021-01-01202111534018.441.24STORE0001GermanyBerlinHypermarket52.5258613.39071SKU0086BrandB ShampooPersonal CareShampooBrandB1610.490.101167.84151.06248011S0087.530.182
12021-01-022021125351012.611.12STORE0001GermanyBerlinHypermarket52.5258613.39071SKU0086BrandB ShampooPersonal CareShampooBrandB1210.490.000125.88125.8823806S0575.190.505
22021-01-032021135361012.022.69STORE0001GermanyBerlinHypermarket52.5258613.39071SKU0086BrandB ShampooPersonal CareShampooBrandB3810.490.301398.62279.0323806S0175.590.168
32021-01-0420211410007.764.65STORE0001GermanyBerlinHypermarket52.5258613.39071SKU0086BrandB ShampooPersonal CareShampooBrandB810.490.00083.9283.9221607S0127.810.255
42021-01-05202115110011.161.77STORE0001GermanyBerlinHypermarket52.5258613.39071SKU0086BrandB ShampooPersonal CareShampooBrandB1710.490.201178.33142.6637208S0387.620.073
52021-01-06202116120013.291.46STORE0001GermanyBerlinHypermarket52.5258613.39071SKU0086BrandB ShampooPersonal CareShampooBrandB1110.490.000115.39115.3935304S0175.350.490
62021-01-0720211713006.1911.58STORE0001GermanyBerlinHypermarket52.5258613.39071SKU0086BrandB ShampooPersonal CareShampooBrandB1510.490.201157.35125.8818309S0037.190.115
72021-01-08202118140013.002.90STORE0001GermanyBerlinHypermarket52.5258613.39071SKU0086BrandB ShampooPersonal CareShampooBrandB810.490.00083.9283.9223905S0076.780.354
82021-01-0920211915109.560.26STORE0001GermanyBerlinHypermarket52.5258613.39071SKU0086BrandB ShampooPersonal CareShampooBrandB1510.490.151157.35133.7527208S0395.380.337
92021-01-102021110161013.420.72STORE0001GermanyBerlinHypermarket52.5258613.39071SKU0086BrandB ShampooPersonal CareShampooBrandB1710.490.101178.33160.5025007S0286.990.234
dateyearmonthdayweekofyearweekdayis_weekendis_holidaytemperaturerain_mmstore_idcountrycitychannellatitudelongitudesku_idsku_namecategorysubcategorybrandunits_soldlist_pricediscount_pctpromo_flaggross_salesnet_salesstock_on_handstock_out_flaglead_time_dayssupplier_idpurchase_costmargin_pct
10999902022-09-032022933551015.421.26STORE0013NetherlandsAmsterdamConvenience52.362314.9585SKU0073BrandA SoftenerHome CareSoftenerBrandA104.990.0049.9049.9024203S0233.360.327
10999912022-09-04202294356107.250.92STORE0013NetherlandsAmsterdamConvenience52.362314.9585SKU0073BrandA SoftenerHome CareSoftenerBrandA64.990.0029.9429.9422908S0102.690.460
10999922022-09-052022953600011.770.06STORE0013NetherlandsAmsterdamConvenience52.362314.9585SKU0073BrandA SoftenerHome CareSoftenerBrandA34.990.0014.9714.9732608S0563.680.262
10999932022-09-06202296361008.774.23STORE0013NetherlandsAmsterdamConvenience52.362314.9585SKU0073BrandA SoftenerHome CareSoftenerBrandA54.990.0024.9524.9526104S0343.510.297
10999942022-09-072022973620011.011.46STORE0013NetherlandsAmsterdamConvenience52.362314.9585SKU0073BrandA SoftenerHome CareSoftenerBrandA74.990.0034.9334.9336106S0503.600.278
10999952022-09-082022983630012.360.05STORE0013NetherlandsAmsterdamConvenience52.362314.9585SKU0073BrandA SoftenerHome CareSoftenerBrandA64.990.0029.9429.9423301S0233.070.384
10999962022-09-092022993640012.515.96STORE0013NetherlandsAmsterdamConvenience52.362314.9585SKU0073BrandA SoftenerHome CareSoftenerBrandA74.990.0034.9334.9321907S0282.530.493
10999972022-09-1020229103651012.986.26STORE0013NetherlandsAmsterdamConvenience52.362314.9585SKU0073BrandA SoftenerHome CareSoftenerBrandA94.990.0044.9144.91305010S0272.460.507
10999982022-09-1120229113661016.720.20STORE0013NetherlandsAmsterdamConvenience52.362314.9585SKU0073BrandA SoftenerHome CareSoftenerBrandA54.990.0024.9524.9528209S0582.920.414
10999992022-09-1220229123700013.413.60STORE0013NetherlandsAmsterdamConvenience52.362314.9585SKU0073BrandA SoftenerHome CareSoftenerBrandA64.990.0029.9429.9420603S0512.870.425